Extrapair mating between relatives in the barn swallow: a role for kin selection?
Bibliographic record
Abstract
Why do females of many species mate with more than one male? One of the main hypotheses suggests that female promiscuity is an insurance mechanism against the potential detrimental effects of inbreeding. Accordingly, females should preferably mate with less related males in multiple or extrapair mating. Here we analyse paternity, relatedness among mating partners, and relatedness between parents and offspring, in the socially monogamous North American barn swallow (Hirundo rustica erythrogaster). In contrast to the inbreeding avoidance hypothesis, we found that extrapair mating partners were more related than expected by random choice, and tended to be more related than social partners. Furthermore, extrapair mating resulted in genetic parents being more related to their extrapair young than to their withinpair young. We propose a new hypothesis for extrapair mating based on kin selection theory as a possible explanation to these findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".